This page has only limited features, please log in for full access.

Unclaimed
Vsevolod Moreido
Water Problems Institute, Russian Academy of Sciences, 11333 Moscow, Russia

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 19 June 2021 in Water
Reads 0
Downloads 0

With more machine learning methods being involved in social and environmental research activities, we are addressing the role of available information for model training in model performance. We tested the abilities of several machine learning models for short-term hydrological forecasting by inferring linkages with all available predictors or only with those pre-selected by a hydrologist. The models used in this study were multivariate linear regression, the M5 model tree, multilayer perceptron (MLP) artificial neural network, and the long short-term memory (LSTM) model. We used two river catchments in contrasting runoff generation conditions to try to infer the ability of different model structures to automatically select the best predictor set from all those available in the dataset and compared models’ performance with that of a model operating on predictors prescribed by a hydrologist. Additionally, we tested how shuffling of the initial dataset improved model performance. We can conclude that in rainfall-driven catchments, the models performed generally better on a dataset prescribed by a hydrologist, while in mixed-snowmelt and baseflow-driven catchments, the automatic selection of predictors was preferable.

ACS Style

Vsevolod Moreido; Boris Gartsman; Dimitri Solomatine; Zoya Suchilina. How Well Can Machine Learning Models Perform without Hydrologists? Application of Rational Feature Selection to Improve Hydrological Forecasting. Water 2021, 13, 1696 .

AMA Style

Vsevolod Moreido, Boris Gartsman, Dimitri Solomatine, Zoya Suchilina. How Well Can Machine Learning Models Perform without Hydrologists? Application of Rational Feature Selection to Improve Hydrological Forecasting. Water. 2021; 13 (12):1696.

Chicago/Turabian Style

Vsevolod Moreido; Boris Gartsman; Dimitri Solomatine; Zoya Suchilina. 2021. "How Well Can Machine Learning Models Perform without Hydrologists? Application of Rational Feature Selection to Improve Hydrological Forecasting." Water 13, no. 12: 1696.

Journal article
Published: 09 May 2020 in Water
Reads 0
Downloads 0

In this study, the downstream effects of pollutants spreading due to hydromorphological gradients and associated changes in sediment transport conditions along the braided-meandering and deltaic distributary reach of a large river downstream section are discussed. We demonstrate the significance of hydrodynamic control for sediment-associated metal partitioning along the river. Typically, the downward decline of the sediment and metals spreading towards Lake Baikal is observed due to buffer effects in the delta. During peak flow, the longitudinal gradients in heavy metal concentration along the distributary delta reach are neglected due to higher concentrations delivered from the upper parts of the river. In particular, significant variations of heavy metal concentrations associated with the river depth are related to sediment concentration and flow velocity profiles. Various particulate metal behavior in silt-sand delta channels and the sand–gravel Selenga main stem emphasize the importance of near-bottom exchange for particles spreading with the river flow. Using empirically derived Rouse numbers, we found quantitative relationships between the ratio of particulate metals sorting throughout depth in a single river channel and the hydrodynamic conditions of sediment transport.

ACS Style

Sergey Chalov; Vsevolod Moreido; Ekaterina Sharapova; Lyudmila Efimova; Vasyli Efimov; Mikhail Lychagin; Nikolay Kasimov. Hydrodynamic Controls of Particulate Metals Partitioning Along the Lower Selenga River—Main Tributary of The Lake Baikal. Water 2020, 12, 1345 .

AMA Style

Sergey Chalov, Vsevolod Moreido, Ekaterina Sharapova, Lyudmila Efimova, Vasyli Efimov, Mikhail Lychagin, Nikolay Kasimov. Hydrodynamic Controls of Particulate Metals Partitioning Along the Lower Selenga River—Main Tributary of The Lake Baikal. Water. 2020; 12 (5):1345.

Chicago/Turabian Style

Sergey Chalov; Vsevolod Moreido; Ekaterina Sharapova; Lyudmila Efimova; Vasyli Efimov; Mikhail Lychagin; Nikolay Kasimov. 2020. "Hydrodynamic Controls of Particulate Metals Partitioning Along the Lower Selenga River—Main Tributary of The Lake Baikal." Water 12, no. 5: 1345.

Preprint content
Published: 23 March 2020
Reads 0
Downloads 0

We present the post-processing technique for the operational ensemble forecasting system (EFS) currently applied to the Cheboksary reservoir on the Volga River in Russia. The operational forecasting system is built around the ECOMAG semi-distributed hydrological model and has shown to produce reliable forecasts of spring snowmelt water inflow into the reservoir on lead-times up to four months ahead (Gelfan et al., 2018). We propose the improvement of the mean reservoir monthly inflow forecast skill by constructing cumulative distributions (CDF) of the observed streamflow conditioned on the predicted streamflow from the EFS and observed mean monthly air temperature and precipitation. We overcome the limitation of short time-series of the observed variables by multivariate modelling procedure allowing for the time-series extension. The extended time-series are then classified into 64 categories each containing the unique combination of the predictors by their quartile values, and the observed monthly inflow CDFs are constructed. The improved operational forecast CDF is consequently picked from the obtained 64 CDF classes by defining the appropriate CDF class from the combination of the raw ensemble forecast and any weather prediction available. The proposed technique was assessed by using the SL-AV weather model (Khan, 2011; Tolstykh, 2017) monthly temperature and precipitation hindcasts for the evaluation period of 1982 – 2010. The forecasts were benchmarked against climate and observed (perfect) weather forecast and have shown improvement in terms of reliability and resolution.

The research is supported by the Russian Science Foundation, project no. 17-77-30006.

References:

Gelfan, A., Moreydo, V., Motovilov, Y., & Solomatine, D. P. (2018). Long-term ensemble forecast of snowmelt inflow into the Cheboksary Reservoir under two different weather scenarios. Hydrology and Earth System Sciences, 22(4). https://doi.org/10.5194/hess-22-2073-2018

Tolstykh, M., Shashkin, V., Fadeev, R., and Goyman, G.: Vorticity-divergence semi-Lagrangian global atmospheric model SL-AV20: dynamical core, Geosci. Model Dev., 10, 1961–1983, https://doi.org/10.5194/gmd-10-1961-2017, 2017

Khan V.M., Kryzhov V.N., Vilfand R.M., Tishchenko V.A., Bundel A.Y. Multimodel approach to seasonal prediction. Russian Meteorology and Hydrology. 2011. Т. 36. № 1. С. 11-17.

ACS Style

Vsevolod Moreydo; Boris Gartsman; Valentina Khan; Vladimir Tischenko. Skill improvement of snow-dominated reservoir inflow forecasts using seasonal weather predictions. 2020, 1 .

AMA Style

Vsevolod Moreydo, Boris Gartsman, Valentina Khan, Vladimir Tischenko. Skill improvement of snow-dominated reservoir inflow forecasts using seasonal weather predictions. . 2020; ():1.

Chicago/Turabian Style

Vsevolod Moreydo; Boris Gartsman; Valentina Khan; Vladimir Tischenko. 2020. "Skill improvement of snow-dominated reservoir inflow forecasts using seasonal weather predictions." , no. : 1.

Conference paper
Published: 01 August 2019 in Proceedings of the International Association of Hydrological Sciences
Reads 0
Downloads 0

Regional climate change affects the flow conditions in river basins which can impact the health of aquatic ecosystems. Potential impacts of future climate scenarios on Coregonus migratorius spawning migration in the Selenga River were assessed. A regional process-based hydrological model was used to reproduce the historical trends in the annual flow and assess its future changes under several climate change scenarios. Annual flow projections were used to identify preferential river reaches for spawning activity of the Arctic cisco (Coregonus migratorius), based on the significant negative correlation of spawning activity with the Selenga River streamflow. The applied methodology shows that the projected decline in runoff of 10 % to 25 % in XXI century may result in shifting of the spawning locations further upstream of the Ulan-Ude city, a local “pollution hotspot”.

ACS Style

Vsevolod Moreydo; Tatiana Millionshchikova; Sergey Chalov. Modelling future hydroclimatic effects on the Coregonus migratorius spawning migration in the Selenga River and Lake Baikal. Proceedings of the International Association of Hydrological Sciences 2019, 381, 113 -119.

AMA Style

Vsevolod Moreydo, Tatiana Millionshchikova, Sergey Chalov. Modelling future hydroclimatic effects on the Coregonus migratorius spawning migration in the Selenga River and Lake Baikal. Proceedings of the International Association of Hydrological Sciences. 2019; 381 ():113-119.

Chicago/Turabian Style

Vsevolod Moreydo; Tatiana Millionshchikova; Sergey Chalov. 2019. "Modelling future hydroclimatic effects on the Coregonus migratorius spawning migration in the Selenga River and Lake Baikal." Proceedings of the International Association of Hydrological Sciences 381, no. : 113-119.

Journal article
Published: 26 July 2019 in Water
Reads 0
Downloads 0

The development and deployment of new operational runoff forecasting systems are a strong focus of the scientific community due to the crucial importance of reliable and timely runoff predictions for early warnings of floods and flashfloods for local businesses and communities. OpenForecast, the first operational runoff forecasting system in Russia, open for public use, is presented in this study. We developed OpenForecast based only on open-source software and data—GR4J hydrological model, ERA-Interim meteorological reanalysis, and ICON deterministic short-range meteorological forecasts. Daily forecasts were generated for two basins in the European part of Russia. Simulation results showed a limited efficiency in reproducing the spring flood of 2019. Although the simulations managed to capture the timing of flood peaks, they failed in estimating flood volume. However, further implementation of the parsimonious data assimilation technique significantly alleviates simulation errors. The revealed limitations of the proposed operational runoff forecasting system provided a foundation to outline its further development and improvement.

ACS Style

Georgy Ayzel; Natalia Varentsova; Oxana Erina; Dmitriy Sokolov; Liubov Kurochkina; Vsevolod Moreydo. OpenForecast: The First Open-Source Operational Runoff Forecasting System in Russia. Water 2019, 11, 1546 .

AMA Style

Georgy Ayzel, Natalia Varentsova, Oxana Erina, Dmitriy Sokolov, Liubov Kurochkina, Vsevolod Moreydo. OpenForecast: The First Open-Source Operational Runoff Forecasting System in Russia. Water. 2019; 11 (8):1546.

Chicago/Turabian Style

Georgy Ayzel; Natalia Varentsova; Oxana Erina; Dmitriy Sokolov; Liubov Kurochkina; Vsevolod Moreydo. 2019. "OpenForecast: The First Open-Source Operational Runoff Forecasting System in Russia." Water 11, no. 8: 1546.

Journal article
Published: 01 October 2018 in Water Resources
Reads 0
Downloads 0

Long-term or seasonal forecasting is crucial for the management of large water systems. Advances in catchment hydrology, such as mathematical models of catchment processes, are proven to be capable of creating reliable streamflow forecasting systems. In this study, the limits of predictability of streamflow in a snowmelt-dominated river basin are examined and a new illustration of the forecast efficiency across different issue dates and lead times—the so-called “forecastability map”—is demonstrated.

ACS Style

V. M. Moreido. Assessment of the Long-Term Hydrological Forecast Skill Evolution across Lead-Times within the Ensemble Streamflow Prediction Framework. Water Resources 2018, 45, 122 -127.

AMA Style

V. M. Moreido. Assessment of the Long-Term Hydrological Forecast Skill Evolution across Lead-Times within the Ensemble Streamflow Prediction Framework. Water Resources. 2018; 45 (1):122-127.

Chicago/Turabian Style

V. M. Moreido. 2018. "Assessment of the Long-Term Hydrological Forecast Skill Evolution across Lead-Times within the Ensemble Streamflow Prediction Framework." Water Resources 45, no. 1: 122-127.

Journal article
Published: 29 December 2017 in Geosciences
Reads 0
Downloads 0

This paper considers the main principles and technologies used in developing the operational modeling system for the Ussuri River Basin of 24,400 km2 based on the automated system of hydrological monitoring and data management (ASHM), the physical-mathematical model with distributed parameters ECOMAG (ECOlogical Model for Applied Geophysics) and the numerical mesoscale atmosphere model WRF (Weather Research and Forecasting Model). The system is designed as a freely combined tool that allows flexible changing of the forecasting and informational components. The technology of inter-model and cross-platform interoperability is based on the use of the Simple Object Access Protocol (SOAP) web services and the Open Geospatial Consortium Open Modelling Interface (OGC OpenMI) standard. The system demonstrates good performance in short-term forecast of rainfall floods and reproduces complex spatio-temporal structure for the runoff formation during extreme rainfall.

ACS Style

Andrei Bugaets; Boris Gartsman; Alexander Gelfan; Yury Motovilov; Oleg Sokolov; Leonid Gonchukov; Andrei Kalugin; Vsevolod Moreido; Zoya Suchilina; Evgeniya Fingert. The Integrated System of Hydrological Forecasting in the Ussuri River Basin Based on the ECOMAG Model. Geosciences 2017, 8, 5 .

AMA Style

Andrei Bugaets, Boris Gartsman, Alexander Gelfan, Yury Motovilov, Oleg Sokolov, Leonid Gonchukov, Andrei Kalugin, Vsevolod Moreido, Zoya Suchilina, Evgeniya Fingert. The Integrated System of Hydrological Forecasting in the Ussuri River Basin Based on the ECOMAG Model. Geosciences. 2017; 8 (1):5.

Chicago/Turabian Style

Andrei Bugaets; Boris Gartsman; Alexander Gelfan; Yury Motovilov; Oleg Sokolov; Leonid Gonchukov; Andrei Kalugin; Vsevolod Moreido; Zoya Suchilina; Evgeniya Fingert. 2017. "The Integrated System of Hydrological Forecasting in the Ussuri River Basin Based on the ECOMAG Model." Geosciences 8, no. 1: 5.

Preprint content
Published: 16 September 2017
Reads 0
Downloads 0
ACS Style

Vsevolod Moreydo. Authors’ responses to the comments of anonymous Reviewer 1. 2017, 1 .

AMA Style

Vsevolod Moreydo. Authors’ responses to the comments of anonymous Reviewer 1. . 2017; ():1.

Chicago/Turabian Style

Vsevolod Moreydo. 2017. "Authors’ responses to the comments of anonymous Reviewer 1." , no. : 1.

Preprint content
Published: 16 September 2017
Reads 0
Downloads 0
ACS Style

Vsevolod Moreydo. Authors’ responses to the comments of anonymous Reviewer 3. 2017, 1 .

AMA Style

Vsevolod Moreydo. Authors’ responses to the comments of anonymous Reviewer 3. . 2017; ():1.

Chicago/Turabian Style

Vsevolod Moreydo. 2017. "Authors’ responses to the comments of anonymous Reviewer 3." , no. : 1.

Preprint content
Published: 16 September 2017
Reads 0
Downloads 0
ACS Style

Vsevolod Moreydo. Authors’ responses to the comments of anonymous Reviewer 2. 2017, 1 .

AMA Style

Vsevolod Moreydo. Authors’ responses to the comments of anonymous Reviewer 2. . 2017; ():1.

Chicago/Turabian Style

Vsevolod Moreydo. 2017. "Authors’ responses to the comments of anonymous Reviewer 2." , no. : 1.

Conference paper
Published: 11 June 2015 in Proceedings of the International Association of Hydrological Sciences
Reads 0
Downloads 0

An approach to seasonal ensemble forecast of unregulated water inflow into a large reservoir was developed. The approach is founded on a physically-based semi-distributed hydrological model ECOMAG driven by Monte-Carlo generated ensembles of weather scenarios for a specified lead-time of the forecast (3 months ahead in this study). Case study was carried out for the Cheboksary reservoir (catchment area is 374 000 km2) located on the middle Volga River. Initial watershed conditions on the forecast date (1 March for spring freshet and 1 June for summer low-water period) were simulated by the hydrological model forced by daily meteorological observations several months prior to the forecast date. A spatially distributed stochastic weather generator was used to produce time-series of daily weather scenarios for the forecast lead-time. Ensemble of daily water inflow into the reservoir was obtained by driving the ECOMAG model with the generated weather time-series. The proposed ensemble forecast technique was verified on the basis of the hindcast simulations for 29 spring and summer seasons beginning from 1982 (the year of the reservoir filling to capacity) to 2010. The verification criteria were used in order to evaluate an ability of the proposed technique to forecast freshet/low-water events of the pre-assigned severity categories.

ACS Style

Alexander Gelfan; Yu. G. Motovilov; V. M. Moreido. Ensemble seasonal forecast of extreme water inflow into a large reservoir. Proceedings of the International Association of Hydrological Sciences 2015, 369, 115 -120.

AMA Style

Alexander Gelfan, Yu. G. Motovilov, V. M. Moreido. Ensemble seasonal forecast of extreme water inflow into a large reservoir. Proceedings of the International Association of Hydrological Sciences. 2015; 369 ():115-120.

Chicago/Turabian Style

Alexander Gelfan; Yu. G. Motovilov; V. M. Moreido. 2015. "Ensemble seasonal forecast of extreme water inflow into a large reservoir." Proceedings of the International Association of Hydrological Sciences 369, no. : 115-120.